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Special Issue "Machine Learning Applications in Power System Condition Monitoring"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Electrical Power System".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 8016

Special Issue Editor

Dr. Bruce Stephen
E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1RD, UK
Interests: condition monitoring; renewable generation; rotating plant; machine learning; load forecasting; load characterization; power networks; meteorological effects; fault prognostics; fault diagnosis

Special Issue Information

Dear Colleagues,

I am inviting submissions for a Special Issue of Energies on the subject area of “Machine Learning Applications in Power System Condition Monitoring”. In recent years, power systems have undergone a once in a generation transformation to accommodate low carbon technologies while supporting ever higher expectations of service level. New technology and legacy plants are expected to co-exist seamlessly on networks that are being used outside of their original design specification through schemes such as dynamic rating. Condition monitoring offers a route to facilitating this but only if data can be reduced to an interpretable form, which is where machine learning offers leverage. Supporting existing domain expertise with higher resolution operational insight unlocks the investment in condition monitoring, and here the design of appropriate analytics and automation is key. Whether at generation, transmission, distribution, or end use, power assets are diverse and their performance is reflective of their health and operating environment. Accordingly, topics of interest for this Special Issue include, but are not limited to:

  • Monitoring of renewable generation
  • Monitoring of legacy assets
  • Transmission and distribution network assets
  • Prognostics for battery energy storage
  • Minimal data availability
  • Condition monitoring of power electronics
  • Explicable machine learning
  • Integration of machine learning with physics based models
Dr. Bruce Stephen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Power networks
  • Machine learning
  • Power generation
  • Renewable generation
  • Anomaly detection
  • Nuclear generation
  • Model selection
  • Fault diagnosis
  • Prognostics
  • Transformers
  • Power system protection
  • Cables
  • Power quality

Published Papers (8 papers)

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Editorial

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Editorial
Machine Learning Applications in Power System Condition Monitoring
Energies 2022, 15(5), 1808; https://doi.org/10.3390/en15051808 - 01 Mar 2022
Viewed by 517
Abstract
While machine learning has made inroads into many industries, power systems have some unique application constraints and barriers that have motivated the creation of this Special Issue on their applications in condition monitoring [...] Full article
(This article belongs to the Special Issue Machine Learning Applications in Power System Condition Monitoring)

Research

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Article
A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines
Energies 2021, 14(11), 3236; https://doi.org/10.3390/en14113236 - 01 Jun 2021
Cited by 9 | Viewed by 952
Abstract
This paper presents a cumulative sum (CUSUM)-based approach for condition monitoring and fault diagnosis of wind turbines (WTs) using SCADA data. The main ideas are to first form a multiple linear regression model using data collected in normal operation state, then monitor the [...] Read more.
This paper presents a cumulative sum (CUSUM)-based approach for condition monitoring and fault diagnosis of wind turbines (WTs) using SCADA data. The main ideas are to first form a multiple linear regression model using data collected in normal operation state, then monitor the stability of regression coefficients of the model on new observations, and detect a structural change in the form of coefficient instability using CUSUM tests. The method is applied for on-line condition monitoring of a WT using temperature-related SCADA data. A sequence of CUSUM test statistics is used as a damage-sensitive feature in a control chart scheme. If the sequence crosses either upper or lower critical line after some recursive regression iterations, then it indicates the occurrence of a fault in the WT. The method is validated using two case studies with known faults. The results show that the method can effectively monitor the WT and reliably detect abnormal problems. Full article
(This article belongs to the Special Issue Machine Learning Applications in Power System Condition Monitoring)
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Article
Weather Related Fault Prediction in Minimally Monitored Distribution Networks
Energies 2021, 14(8), 2053; https://doi.org/10.3390/en14082053 - 07 Apr 2021
Cited by 2 | Viewed by 698
Abstract
Power distribution networks are increasingly challenged by ageing plant, environmental extremes and previously unforeseen operational factors. The combination of high loading and weather conditions is responsible for large numbers of recurring faults in legacy plants which have an impact on service quality. Owing [...] Read more.
Power distribution networks are increasingly challenged by ageing plant, environmental extremes and previously unforeseen operational factors. The combination of high loading and weather conditions is responsible for large numbers of recurring faults in legacy plants which have an impact on service quality. Owing to their scale and dispersed nature, it is prohibitively expensive to intensively monitor distribution networks to capture the electrical context these disruptions occur in, making it difficult to forestall recurring faults. In this paper, localised weather data are shown to support fault prediction on distribution networks. Operational data are temporally aligned with meteorological observations to identify recurring fault causes with the potentially complex relation between them learned from historical fault records. Five years of data from a UK Distribution Network Operator is used to demonstrate the approach at both HV and LV distribution network levels with results showing the ability to predict the occurrence of a weather related fault at a given substation considering only meteorological observations. Unifying a diverse range of previously identified fault relations in a single ensemble model and accompanying the predicted network conditions with an uncertainty measure would allow a network operator to manage their network more effectively in the long term and take evasive action for imminent events over shorter timescales. Full article
(This article belongs to the Special Issue Machine Learning Applications in Power System Condition Monitoring)
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Article
Combination of Thermal Modelling and Machine Learning Approaches for Fault Detection in Wind Turbine Gearboxes
Energies 2021, 14(5), 1375; https://doi.org/10.3390/en14051375 - 03 Mar 2021
Cited by 7 | Viewed by 940
Abstract
This research aims to bring together thermal modelling and machine learning approaches to improve the understanding on the operation and fault detection of a wind turbine gearbox. Recent fault detection research has focused on machine learning, black box approaches. Although it can be [...] Read more.
This research aims to bring together thermal modelling and machine learning approaches to improve the understanding on the operation and fault detection of a wind turbine gearbox. Recent fault detection research has focused on machine learning, black box approaches. Although it can be successful, it provides no indication of the physical behaviour. In this paper, thermal network modelling was applied to two datasets using SCADA (Supervisory Control and Data Acquisition) temperature data, with the aim of detecting a fault one month before failure. A machine learning approach was used on the same data to compare the results to thermal modelling. The results found that thermal network modelling could successfully detect a fault in many of the turbines examined and was validated by the machine learning approach for one of the datasets. For that same dataset, it was found that combining the thermal model losses and the machine learning approach by using the modelled losses as a feature in the classifier resulted in the engineered feature becoming the most important feature in the classifier. It was also found that the results from thermal modelling had a significantly greater effect on successfully classifying the health of a turbine compared to temperature data. The other dataset gave less conclusive results, suggesting that the location of the fault and the temperature sensors could impact the fault-detection ability. Full article
(This article belongs to the Special Issue Machine Learning Applications in Power System Condition Monitoring)
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Article
A Stacked Denoising Sparse Autoencoder Based Fault Early Warning Method for Feedwater Heater Performance Degradation
Energies 2020, 13(22), 6061; https://doi.org/10.3390/en13226061 - 19 Nov 2020
Cited by 2 | Viewed by 559
Abstract
Power grid operation faces severe challenges with the increasing integration of intermittent renewable energies. Hence the steam turbine, which mainly undertakes the task of frequency regulation and peak shaving, always operates under off-design conditions to meet the accommodation demand. This would affect the [...] Read more.
Power grid operation faces severe challenges with the increasing integration of intermittent renewable energies. Hence the steam turbine, which mainly undertakes the task of frequency regulation and peak shaving, always operates under off-design conditions to meet the accommodation demand. This would affect the operation economy and exacerbate the ullage of equipment. The feedwater heater (FWH) plays an important role in unit, whose timely fault early warning is significant in improving the operational reliability of unit. Therefore, this paper proposes a stacked denoising sparse autoencoder (SDSAE) based fault early warning method for FWH. Firstly, the concept of a frequent pattern model is proposed as an indicator of FWH performance evaluation. Then, an SDSAE- back-propagation (BP) based method is introduced to achieve self-adaptive feature reduction and depict nonlinear properties of frequent pattern modeling. By experimenting with actual data, the feasibility and validity of the proposed method are verified. Its detection accuracy reaches 99.58% and 100% for normal and fault data, respectively. Finally, competitive experiments prove the necessity of feature reduction and the superiority of SDSAE based feature reduction compared with traditional methods. This paper puts forward a precise and effective method to serve for FWH fault early warning and refines the key issues to inspire later researchers. Full article
(This article belongs to the Special Issue Machine Learning Applications in Power System Condition Monitoring)
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Article
Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data
Energies 2020, 13(19), 5152; https://doi.org/10.3390/en13195152 - 02 Oct 2020
Cited by 13 | Viewed by 1148
Abstract
Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O & M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison [...] Read more.
Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O & M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison were One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Elliptical Envelope (EE). Each of these were compared for the same fault, and tested under various different data configurations. IF and EE have not previously been used for fault detection for wind turbines, and OCSVM has not been used for SCADA data. This paper presents a novel method of condition monitoring that only requires two months of data per turbine. These months were separated by a year, the first being healthy and the second unhealthy. The number of anomalies is compared, with a greater number in the unhealthy month being considered correct. It was found that for accuracy IF and OCSVM had similar performances in both training regimes presented. OCSVM performed better for generic training, and IF performed better for specific training. Overall, IF and OCSVM had an average accuracy of 82% for all configurations considered, compared to 77% for EE. Full article
(This article belongs to the Special Issue Machine Learning Applications in Power System Condition Monitoring)
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Article
Effect of Time History on Normal Behaviour Modelling Using SCADA Data to Predict Wind Turbine Failures
Energies 2020, 13(18), 4745; https://doi.org/10.3390/en13184745 - 11 Sep 2020
Cited by 8 | Viewed by 1099
Abstract
Operations and Maintenance (O&M) can make up a significant proportion of lifetime costs associated with any wind farm, with up to 30% reported for some offshore developments. It is increasingly important for wind farm owners and operators to optimise their assets in order [...] Read more.
Operations and Maintenance (O&M) can make up a significant proportion of lifetime costs associated with any wind farm, with up to 30% reported for some offshore developments. It is increasingly important for wind farm owners and operators to optimise their assets in order to reduce the levelised cost of energy (LCoE). Reducing downtime through condition-based maintenance is a promising strategy of realising these goals. This is made possible through increased monitoring and gathering of operational data. SCADA data are useful in terms of wind turbine condition monitoring. This paper aims to perform a comprehensive comparison between two types of normal behaviour modelling: full signal reconstruction (FSRC) and autoregressive models with exogenous inputs (ARX). At the same time, the effects of the training time period on model performance are explored by considering models trained with both 12 and 6 months of data. Finally, the effects of time resolution are analysed for each algorithm by considering models trained and tested with both 10 and 60 min averaged data. Two different cases of wind turbine faults are examined. In both cases, the NARX model trained with 12 months of 10 min average Supervisory Control And Data Acquisition (SCADA) data had the best training performance. Full article
(This article belongs to the Special Issue Machine Learning Applications in Power System Condition Monitoring)
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Article
Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems
Energies 2020, 13(18), 4676; https://doi.org/10.3390/en13184676 - 08 Sep 2020
Cited by 12 | Viewed by 1081
Abstract
Decarbonization of marine transport is a key global issue, with the carbon emissions of international shipping projected to increase 23% to 1090 million tonnes by 2035 in comparison to 2015 levels. Optimization of the energy system (especially propulsion system) in these vessels is [...] Read more.
Decarbonization of marine transport is a key global issue, with the carbon emissions of international shipping projected to increase 23% to 1090 million tonnes by 2035 in comparison to 2015 levels. Optimization of the energy system (especially propulsion system) in these vessels is a complex multi-objective challenge involving economical maintenance, environmental metrics, and energy demand requirements. In this paper, data from instrumented vessels on the River Thames in London, which includes environmental emissions, power demands, journey patterns, and variance in operational patterns from the captain(s) and loading (passenger numbers), is integrated and analyzed through automatic, multi-objective global optimization to create an optimal hybrid propulsion configuration for a hybrid vessel. We propose and analyze a number of computational techniques, both for monitoring and remaining useful lifetime (RUL) estimation of individual energy assets, as well as modeling and optimization of energy use scenarios of a hybrid-powered vessel. Our multi-objective optimization relates to emissions, asset health, and power performance. We show that, irrespective of the battery packs used, our Relevance Vector Machine (RVM) algorithm is able to achieve over 92% accuracy in remaining useful life (RUL) predictions. A k-nearest neighbors algorithm (KNN) is proposed for prognostics of state of charge (SOC) of back-up lead-acid batteries. The classifier achieved an average of 95.5% accuracy in a three-fold cross validation. Utilizing operational data from the vessel, optimal autonomous propulsion strategies are modeled combining the use of battery and diesel engines. The experiment results show that 70% to 80% of fuel saving can be achieved when the diesel engine is operated up to 350 kW. Our methodology has demonstrated the feasibility of combination of artificial intelligence (AI) methods and real world data in decarbonization and optimization of green technologies for maritime propulsion. Full article
(This article belongs to the Special Issue Machine Learning Applications in Power System Condition Monitoring)
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